{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing models\n",
    "\n",
    "In this notebook, we will compare the three models we've trained using:\n",
    "\n",
    "- Aggregate metrics\n",
    "- Performance visualizations\n",
    "- Dataset visualization\n",
    "\n",
    "First, we load data and the three existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/emmanuel.ameisen/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report\n",
    "from sklearn.calibration import calibration_curve\n",
    "from sklearn.metrics import brier_score_loss\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "np.random.seed(35)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from ml_editor.data_processing import (\n",
    "    format_raw_df,\n",
    "    get_split_by_author,\n",
    "    get_vectorized_series, \n",
    "    get_feature_vector_and_label\n",
    ")\n",
    "from ml_editor.model_evaluation import get_feature_importance, get_calibration_plot\n",
    "from ml_editor.model_v2 import POS_NAMES\n",
    "\n",
    "data_path = Path('../data/writers_with_features.csv')\n",
    "df = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_1 = joblib.load(Path(\"../models/model_1.pkl\")) \n",
    "vectorizer_1 = joblib.load(Path(\"../models/vectorizer_1.pkl\")) \n",
    "clf_2 = joblib.load(Path(\"../models/model_2.pkl\"))\n",
    "vectorizer_2 = joblib.load(Path(\"../models/vectorizer_2.pkl\")) \n",
    "\n",
    "# clf_3 does not vectorize text\n",
    "clf_3 = joblib.load(Path(\"../models/model_3.pkl\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = get_split_by_author(df, test_size=0.2, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then score the test data using all three models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"vectors\"] = get_vectorized_series(train_df[\"full_text\"].copy(), vectorizer_1)\n",
    "test_df[\"vectors\"] = get_vectorized_series(test_df[\"full_text\"].copy(), vectorizer_1)\n",
    "\n",
    "features_1 = [\n",
    "                \"action_verb_full\",\n",
    "                \"question_mark_full\",\n",
    "                \"text_len\",\n",
    "                \"language_question\",\n",
    "            ]\n",
    "\n",
    "features_2 = [\"num_questions\", \n",
    "               \"num_periods\",\n",
    "               \"num_commas\",\n",
    "               \"num_exclam\",\n",
    "               \"num_quotes\",\n",
    "               \"num_colon\",\n",
    "               \"num_stops\",\n",
    "               \"num_semicolon\",\n",
    "               \"num_words\",\n",
    "               \"num_chars\",\n",
    "               \"num_diff_words\",\n",
    "               \"avg_word_len\",\n",
    "               \"polarity\"\n",
    "              ]\n",
    "features_2.extend(POS_NAMES.keys())\n",
    "\n",
    "X_test_1, y_test = get_feature_vector_and_label(test_df, features_1)\n",
    "X_test_2, y_test = get_feature_vector_and_label(test_df, features_2)\n",
    "X_test_3 = test_df[features_2].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf1_predicted_proba = clf_1.predict_proba(X_test_1)\n",
    "clf2_predicted_proba = clf_2.predict_proba(X_test_2)\n",
    "clf3_predicted_proba = clf_3.predict_proba(X_test_3)\n",
    "\n",
    "clf1_predicted = clf_1.predict(X_test_1)\n",
    "clf2_predicted = clf_2.predict(X_test_2)\n",
    "clf3_predicted = clf_3.predict(X_test_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing accuracy\n",
    "\n",
    "First, we can compare aggregate scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1: Validation accuracy = 0.614, precision = 0.622, recall = 0.537, f1 = 0.577\n",
      "Model 2: Validation accuracy = 0.618, precision = 0.632, recall = 0.524, f1 = 0.573\n",
      "Model 3: Validation accuracy = 0.584, precision = 0.597, recall = 0.461, f1 = 0.520\n"
     ]
    }
   ],
   "source": [
    "def get_metrics(y_test, y_predicted):  \n",
    "    # true positives / (true positives+false positives)\n",
    "    precision = precision_score(y_test, y_predicted, pos_label=True,\n",
    "                                    average='binary')             \n",
    "    # true positives / (true positives + false negatives)\n",
    "    recall = recall_score(y_test, y_predicted, pos_label=True,\n",
    "                              average='binary')\n",
    "    \n",
    "    # harmonic mean of precision and recall\n",
    "    f1 = f1_score(y_test, y_predicted, pos_label=True, average='binary')\n",
    "    \n",
    "    # true positives + true negatives/ total\n",
    "    accuracy = accuracy_score(y_test, y_predicted)\n",
    "    return accuracy, precision, recall, f1\n",
    "\n",
    "for i, y_predicted in enumerate([clf1_predicted, clf2_predicted, clf3_predicted]):\n",
    "    accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted)\n",
    "    print(\"Model %s: Validation accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f\" % ((i+1), accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing feature importance\n",
    "\n",
    "Let's look at which features each model leverages next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_importance(clf, feature_names, k=10):\n",
    "    print(\"Top %s importances:\\n\" % k)\n",
    "    print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, feature_names)[:k]]))\n",
    "\n",
    "    print(\"\\nBottom %s importances:\\n\" % k)\n",
    "    print('\\n'.join([\"%s: %.2g\" % (tup[0], tup[1]) for tup in get_feature_importance(clf, feature_names)[-k:]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1\n",
      "Top 10 importances:\n",
      "\n",
      "text_len: 0.0091\n",
      "are: 0.006\n",
      "what: 0.0051\n",
      "writing: 0.0048\n",
      "can: 0.0043\n",
      "ve: 0.0041\n",
      "on: 0.0039\n",
      "not: 0.0039\n",
      "story: 0.0039\n",
      "as: 0.0038\n",
      "\n",
      "Bottom 10 importances:\n",
      "\n",
      "horrifying: 0\n",
      "succeeds: 0\n",
      "fired: 0\n",
      "settling: 0\n",
      "settled: 0\n",
      "horrific: 0\n",
      "cms: 0\n",
      "pulling: 0\n",
      "coast: 0\n",
      "moonlight: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Model 1\")\n",
    "w_indices = vectorizer_1.get_feature_names()\n",
    "w_indices.extend(features_1)\n",
    "all_feature_1 = np.array(w_indices)\n",
    "display_importance(clf_1, all_feature_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 2\n",
      "Top 10 importances:\n",
      "\n",
      "num_questions: 0.0078\n",
      "num_periods: 0.0078\n",
      "num_chars: 0.0078\n",
      "num_diff_words: 0.0078\n",
      "DET: 0.0072\n",
      "ADJ: 0.007\n",
      "ADV: 0.0068\n",
      "num_commas: 0.0067\n",
      "NOUN: 0.0065\n",
      "VERB: 0.0063\n",
      "\n",
      "Bottom 10 importances:\n",
      "\n",
      "sped: 0\n",
      "odyssey: 0\n",
      "er: 0\n",
      "erased: 0\n",
      "obtaining: 0\n",
      "spinning: 0\n",
      "obsessed: 0\n",
      "observations: 0\n",
      "est: 0\n",
      "00: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Model 2\")\n",
    "w_indices = vectorizer_2.get_feature_names()\n",
    "w_indices.extend(features_2)\n",
    "all_feature_2 = np.array(w_indices)\n",
    "display_importance(clf_2, all_feature_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 3\n",
      "Top 10 importances:\n",
      "\n",
      "num_chars: 0.053\n",
      "num_questions: 0.051\n",
      "num_periods: 0.051\n",
      "ADV: 0.049\n",
      "ADJ: 0.049\n",
      "num_diff_words: 0.048\n",
      "DET: 0.048\n",
      "NOUN: 0.045\n",
      "VERB: 0.045\n",
      "PUNCT: 0.045\n",
      "\n",
      "Bottom 10 importances:\n",
      "\n",
      "num_colon: 0.022\n",
      "num_quotes: 0.02\n",
      "AUX: 0.016\n",
      "SYM: 0.015\n",
      "INTJ: 0.014\n",
      "X: 0.011\n",
      "num_semicolon: 0.0076\n",
      "num_exclam: 0.0072\n",
      "CONJ: 0\n",
      "SCONJ: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Model 3\")\n",
    "display_importance(clf_3, np.array(features_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing calibration\n",
    "\n",
    "Next, we'll look at calibration, which is very important in an application were we want to show users meaningful scores representing the quality of their questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_multiple_calibration_plot(predicted_proba_arrays, true_y, figsize=(10, 8)):\n",
    "    \"\"\"\n",
    "    Inspired by sklearn example\n",
    "    https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html\n",
    "    :param figsize: size of the output figure\n",
    "    :param predicted_proba_y: the predicted probabilities of our model for each example\n",
    "    :param true_y: the true value of the label\n",
    "    :return: calibration plot\n",
    "    \"\"\"\n",
    "\n",
    "    plt.figure(figsize=figsize)\n",
    "    ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "    ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "    ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "#     print(\"\\tBrier: %1.3f\" % clf_score)\n",
    "    \n",
    "    for i,predicted_proba_y in enumerate(predicted_proba_arrays):\n",
    "        fraction_of_positives, mean_predicted_value = calibration_curve(\n",
    "            true_y, predicted_proba_y, n_bins=10\n",
    "        )\n",
    "\n",
    "        ax1.plot(\n",
    "            mean_predicted_value,\n",
    "            fraction_of_positives,\n",
    "            \"s-\",\n",
    "            label = \"Model %s\" % (i + 1)\n",
    "        )\n",
    "\n",
    "        ax2.hist(\n",
    "            predicted_proba_y,\n",
    "            range=(0, 1),\n",
    "            bins=10,\n",
    "            histtype=\"step\",\n",
    "            label = \"Model %s\" % (i + 1),\n",
    "            lw=2,\n",
    "        )\n",
    "\n",
    "    ax1.set_ylabel(\"Fraction of positives\")\n",
    "    ax1.set_xlim([0, 1])\n",
    "    ax1.set_ylim([0, 1])\n",
    "    ax1.legend(loc=\"lower right\")\n",
    "    ax1.set_title(\"Calibration plot\")\n",
    "\n",
    "    ax2.set_title(\"Probability distribution\")\n",
    "    ax2.set_xlabel(\"Mean predicted value\")\n",
    "    ax2.set_ylabel(\"Count\")\n",
    "    ax2.legend(loc=\"upper right\", ncol=2)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = [clf1_predicted_proba[:,1], clf2_predicted_proba[:,1], clf3_predicted_proba[:,1]]\n",
    "\n",
    "get_multiple_calibration_plot(predictions, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(clf1_predicted_proba[:,1], y_test, figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.234\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(clf2_predicted_proba[:,1], y_test, figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tBrier: 0.238\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_calibration_plot(clf3_predicted_proba[:,1], y_test, figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third model is showing much better calibration, which is crucial to our application since we want the scores we show users to be as meaningful as possible. In addition, the third model is using the most interpretable features, which will allow us to make clear suggestions. We will use it for the ML Editor."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml_editor",
   "language": "python",
   "name": "ml_editor"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
